我有一张这样的桌子
timestamp avg_hr hr_quality avg_rr rr_quality activity sleep_summary_id
1422404668 66 229 0 0 13 78
1422404670 64 223 0 0 20 78
1422404672 64 216 0 0 11 78
1422404674 66 198 0 40 9 78
1422404676 65 184 0 30 3 78
1422404678 64 173 0 10 17 78
1422404680 66 199 0 20 118 78
我正在尝试按以下方式对数据进行分组timestamp
,sleep id
and rr_quality
, where rr_quality
is > 0
我尝试过以下方法,但似乎都不起作用
df3 = df2.groupby([df2.index.hour,'sleep_summary_id',df2['rr_quality']>0])
df3 = df2.groupby([df2.index.hour,'sleep_summary_id','rr_quality'>0])
df3 = df2.groupby([df2.index.hour,'sleep_summary_id',['rr_quality']>0])
它们都返回一个关键错误。
EDIT:
似乎也无法一次通过多个过滤器。
我尝试了以下方法:
df2[df2['rr_quality'] >= 150, df2['hr_quality'] > 200]
df2[df2['rr_quality'] >= 150, ['hr_quality'] > 200]
df2[[df2['rr_quality'] >= 150, ['hr_quality'] > 200]]
返回:TypeError: 'Series' objects are mutable, thus they cannot be hashed